Summary
Objectives:
Combination of multiple testing and modeling techniques in dose-response studies.
Use of hypotheses tests to assess the significance of the dose-response signal associated
with a given candidate dose-response model. Estimation of target dose(s) following
the previous model selection step. Illustration of the method with a real data example.
Methods:
We assume a set of candidate models potentially reflecting the data generating process.
The appropriateness of each individual model is evaluated in terms of contrast tests,
where each set of contrast weights describes a specific dose-response shape. Optimum
contrast weights are computed, which maxi-mize the non-centrality parameters associated
with the contrast tests. A reference set of appropriate candidate models is obtained
while controlling the familywise error rate. A single model is then selected from
this reference set using standard model selection criteria. The final step is devoted
to dose finding by applying inverse regression techniques. This is illustrated for
estimating the minimum effective dose.
Results:
The method is as powerful as competing standard dose-response tests to detect an
overall dose-related trend. In addition, the possibility is given to estimate one
or more target doses of interest. The analysis of a real data example confirms the
advantages of the proposed hybrid method.
Conclusions:
Combining multiple testing and modeling techniques leads to a powerful tool, which
uses the advantages of both approaches: Rigid error control at the significance testing
step and flexibility at the dose estimation step. The method can be extended to handle
more general linear models including covariates and factorial treatment structures.
Keywords
Dose-response - multiple testing - multiple comparison procedures - regression analysis
- minimum effective dose - calibration
